Images acquired utilizing image capturing devices such as, for example, a digital camera, may require image processing before the image is employed for an intended task (e.g., iris matching). Image degradation, also known as motion blur, is common in images recorded utilizing digital cameras in industrial and scientific applications, which often require monitoring of high-speed events. Motion blur is also a problem stemming from the use of surveillance cameras that track moving objects and cameras mounted on a moving vehicle such as, for example, an aircraft. Motion blur significantly degrades the visual quality of an image and must be prevented during image capturing or mitigated by post-processing of the image to remove the motion blur. Restoration of a blurred and noisy image sequence potentially increases the amount of information that a human observer can obtain from an image sequence.
Most prior art approaches are capable of restoring degraded images, but only to an extent. One prior art approach, for example, employs additional hardware and special sensors in order to estimate and correct the motion of the camera during the exposure time. Another prior art technique involves the use of an imaging system that detects a single motion blur kernel (also referred as “point spread function”) in order for the entire image to be restored as the original image, but only up to a particular level of accuracy. Such an image restoration approach utilizes prior knowledge that assumes a simple camera shutter open/close sequence, which restricts the estimation to a much smaller set of kernels. The use of such an approach is difficult in establishing a universal model for the blur estimation process.
Based on the foregoing, it is believed that a need exists for an improved method and system for estimating motion blur associated with an image captured from an image capturing device. A need also exists for explicitly estimating a family of kernels as a function of a shuttering sequence, as described in greater detail herein.